Using a Continuous Hydrologic Model in Support of Flash Flood Predictions

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Improving Flash Food Prediction in Multiple Environments

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Patrick D. Broxton

Peter A. Troch, Michael Schaffner, Carl Unkrich, David Goodrich, Hoshin Gupta, Thorsten Wagener, Soni Yatheendradas

Motivation: Considerations for Modeling Extreme Streamflow Events

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Wet DryWarm

CoolLow Potential ET

Less Water in StorageMore Water in Storage

High Potential ET

• What is a catchment’s ability to absorb precipitation?

Precipitation

Runoff

BaseflowInfiltration

Motivation: Considerations for Modeling Extreme Streamflow Events

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• What is the “true” precipitation input?Rain Gauges Radar Satellite Observations

More Accurate Less AccurateLess Coverage More Coverage

• What about Snow?

Large Scale

Small Scale

SM-hsB Overview 4

1) Keep track of the hydrologic state between flood model runs2) Distributed so that it can account for spatial variability of terrain and atmospheric forcing

Soil Moisture – hillslope Bousinesq Model

- Water and energy balance at the land surfaceLand Surface Module

- Incorporates Snow

Transmission Zone

Root Zone

hsB Aquifer

Deep Aquifer

Infiltration

ET

Subsurface Module- Root zone water balance- Lateral transport of soil water

Study Sites

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- Five watersheds in New York’s Catskill Mountains:- Humid catchments that are focus of current efforts

Study Sites – New York Watersheds 6

a) W. Branch Delaware River (332 sq mi)

b) W. Branch Delaware River (134 sq mi)

d) East Brook (25 sq mi)

c) Platte Kill (35 sq mi)

e) Town Brook (14 sq mi)

- Three watersheds in southeastern Arizona:- Semi-arid catchments to compliment humid catchments

Study Sites – Arizona Watersheds 7

a) Sabino Canyon (35.5 sq mi)

b) Rincon Creek (44.8 sq mi)

c) Walnut Gulch (57.7 sq mi)

Date

Hydrology of New York Watersheds 8

Month

0

0.4

0.8

1.6

1.2

Delaware River (Walton)Delaware River (Delhi)East BrookTown BrookPlate Kill

Longitude (degrees)

42.2

42.3

42.4

42.575.2 -75 -74.8 -74.6

1050

1100

1150

1200

New York Basins

Longitude (degrees)

31.8

32

32.2

32.4

-111 -110.6 -110.2 -109.8

PRISM – Average Yearly Precipitation (mm)

300

400

600

500

700

800

900

Month

0

0.2

0.6

0.4

0.8 Sabino CanyonRincon CreekWalnut Gulch

Arizona Basins

Modeling with

SM-hsB

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Fully distributed, runs on hourly timesteps (diurnal cycle is important)

Based on energy balance principles – similar to Utah Energy Balance Model

Wet Canopy Evaporation/Snow Interception

Long Wave Radiation

Trees

Precipitation

Infiltration/Runoff

Variable Canopy Cover

Stream

WintertimeSnowpack

ShortwaveRadiation

Near-SurfaceSoil Layer

Atmospheric Inputs:Shortwave RadiationLongwave RadiationPrecipitationTemperaturePressureHumidity

Land Surface Module - Overview 10

Can be run at a point:e.g. Calibrate to a point measurement such as a snow pillow

...or over an area:e.g. Calibrate over an area to remotely sensed data or to a data assimilation system

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SNODAS SWE (mm)

R2 = 0.81

0

20

4060

80

100120

140

0 20 40 60 80 100 120 140

Land Surface Module - Calibration

1/1/2007 4/1/2007 1/1/2008 4/1/20080

40

80

120

0

40

80

120

Photo courtesy Jim Porter at NYCDEP

Over a multi-year span, it is generally tuned to compare well with SNODAS, but for specific years, it can be refined using other measurements

Preliminary results for 2009-2010 Snow Season in W. Branch Delaware River Watershed

February 28,2010

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February 15,2010January 25,2010

4/1/201012/1/2009 1/1/2010 2/1/2010 3/1/20100

Date

40

80

120

160

200 January 15,2010100 mm

0

50 mm

Land Surface Module - Simulation

All precipitation inputs are derived from the MPE

Transmission Zone

Root Zone

hsB Aquifer

Deep Aquifer

hsB Aq. Baseflow

Deep Aq. Baseflow

Infiltration

Runoff

ET

Streamflow Routing

Semi distributed, runs on daily or hourly timesteps

Subsurface Module - Overview 13

Root Zone Water Balance / Baseflow

Deep Aquifer

HSB AquiferBaseflowStreamflow

Runoff

1/1/2005 4/2/2006 7/3/20070

0 5 10 15

10/1/2008 12/31/2009

10

20

30

40

50

60

70

Effective Time (days)

Date

35

0

1

2

-1

20 25 30

Calibration procedure relies on a baseflow separationPortions of the model are reconstructed from the steamflow signatures (hydrology backwards)

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Calibration procedure based on that developed by Gustavo Carrillo and Peter Troch at the University of Arizona

Subsurface Module - Calibration

Catchment NSE Baseflow NSE Streamflow

Delaware River (Walton) 0.61 0.34

Delaware River (Delhi) 0.62 0.10

East Brook 0.58 0.48

Town Brook 0.65 0.41

Platte Kill 0.61 0.34

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Catchment NSE Baseflow NSE Streamflow

Sabino Canyon 0.10 0.41

Rincon Creek -3.34 -0.96

Walnut Gulch No Baseflow

Normalized Water Year Precipitation0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

DataModel

0 20 40 60 80 10010-1

100

101

Probability of Exeedance

DataModel

Subsurface Module - Simulation

ModelData

ModelData

1/1/2005 4/2/2006 7/3/2007 10/1/2008 12/31/20090

20

40

60

0

5

10

15

20

1/1/2005 4/2/2006 7/3/2007 10/1/2008 12/31/2009

Simulation for Delaware River (Walton) using MPE as input

Modeled soil moisture

Modeled water storage

Aquifer depth

Storage-discharge relationships that can be inverted to estimate precipitation from streamflow

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Yields many useful modeled quantities for flood forecastingBaseflow

ModelData

Modeled Soil Moisture

Modeled Transpiration

1/1/2005 4/2/2006 7/3/2007 10/1/2008 12/31/2009

1/1/2005 4/2/2006 7/3/2007 10/1/2008 12/31/2009

1/1/2005 4/2/2006 7/3/2007 10/1/2008 12/31/2009

20

10

0

20

30

40

0

5

hsB Aquifer Storage

Discharge (mm)

0

10

20

30

0 5 10 15 20

Benefits of Modeling With SM-hsB

Potential and actual evapotranspiration

Precipitaiton Estimates

Initial Conditions

Snow and SnowmeltModeled SWE

Summary 17

hsB-SM has been implemented in all NY watersheds, most AZ watersheds

Snow module reproduces wintertime snowpacks; subsurface module works well in the W. Branch Delaware River Basin

Model yields useful information such as snowmelt rates, estimates of catchment “wetness”, and can be useful for estimating rainfall/snowmelt from streamflow response

Although it has not yet been coupled with a flash flood model (KINEROS2), statistical combinations of rainfall and e.g. soil moisture suggest that there is information to be gained from using model data

Total Precip Soil Moisture Combined

Deleware River (Walton) 0.80 0.43 0.88Deleware River (Delhi) 0.65 0.59 0.82East Brook 0.02 0.00 0.06Town Brook 0.68 0.01 0.82Platte Kill 0.48 0.05 0.72AVERAGE 0.52 0.22 0.66

Correlation with Flood Size - Top 10 Events

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Acknowlegements

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Funding comes from a COMET grant (UCAR Award S09-75794)Special thanks to Mike Schaffner, Peter Troch, Gustavo Carrillo, Jim Porter, Glenn Horton, and others

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Questions

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